The Challenge
Cardiovascular disease (CVD) impacts millions of people, with many cases remaining undiagnosed due to limitations in existing diagnostic methods. Non-invasive procedures like Carotid Intima-Media Thickness (C-IMT) testing are vital for assessing vascular health, identifying plaques, and diagnosing CVD. However, traditional C-IMT processes rely on specialized ultrasound equipment and highly trained technicians, resulting in delays that can take weeks before critical interventions are initiated.
The Solution
To address these challenges, we developed an innovative computer vision solution:
- Advanced Computer Vision Models: We trained models to process DICOM images, accurately measuring intima-media thickness and detecting plaques within the carotid artery.
- Automated Reporting Pipelines: The model outputs were integrated into a streamlined digital reporting system, enabling care teams to access diagnostic insights rapidly.
Key Outcomes
- Reduced Diagnostic Time: Care teams now receive and share results in minutes, significantly accelerating treatment timelines.
- Scalable and Accurate Screening: The automated solution provided consistent, reliable results while reducing dependence on specialized technicians.
- Improved Early Detection: Enabled timely detection and monitoring of cardiovascular conditions, increasing the potential for life-saving interventions.
This project demonstrates the power of computer vision in revolutionizing diagnostic workflows, delivering faster, more accurate insights to healthcare providers, and enhancing patient outcomes.
